Nous Research·Llama 3·LlamaForCausalLM

Hermes 2 Theta Llama 3 70B — Hardware Requirements & GPU Compatibility

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Hermes 2 Theta Llama 3 70B is a 70.6B-parameter open language model from Nous Research in the Llama 3 family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 43.30 GB of VRAM — see which GPUs and Macs can run it below.

1.3K downloads 82 likes8K context

Specifications

Publisher
Nous Research
Family
Llama 3
Parameters
70.6B
Architecture
LlamaForCausalLM
Context Length
8,192 tokens
Vocabulary Size
128,256
License
Llama 3 Community

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How Much VRAM Does Hermes 2 Theta Llama 3 70B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.4031.0 GB
Q3_K_S3.5031.8 GB
Q3_K_M3.9035.4 GB
Q4_04.0036.3 GB
Q4_K_M4.8043.3 GB
Q5_K_M5.7051.2 GB
Q6_K6.6059.2 GB
Q8_08.0071.5 GB

Which GPUs Can Run Hermes 2 Theta Llama 3 70B?

Q4_K_M · 43.3 GB

Hermes 2 Theta Llama 3 70B (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 8K context window can add up to 2.0 GB, bringing total usage to 45.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Hermes 2 Theta Llama 3 70B?

Q4_K_M · 43.3 GB

11 devices with unified memory can run Hermes 2 Theta Llama 3 70B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).

Benchmarks

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Related Models

Frequently Asked Questions

How much VRAM does Hermes 2 Theta Llama 3 70B need?

Hermes 2 Theta Llama 3 70B requires 43.3 GB of VRAM at Q4_K_M, or 71.5 GB at Q8_0. Full 8K context adds up to 2.0 GB (45.3 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB

KV Cache + Overhead 1 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 3 GB (at full 8K context)

VRAM usage by quantization

43.3 GB
45.3 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Hermes 2 Theta Llama 3 70B?

Yes, at IQ2_S (23.0 GB) or lower. Higher quantizations like IQ2_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Hermes 2 Theta Llama 3 70B?

For Hermes 2 Theta Llama 3 70B, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (44.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 20.4 GB.

VRAM requirement by quantization

IQ2_XXS
20.4 GB
IQ3_XS
30.1 GB
Q4_0
36.3 GB
Q4_K_M
43.3 GB
Q4_K_L
44.2 GB
Q8_0
71.5 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Hermes 2 Theta Llama 3 70B on a Mac?

Hermes 2 Theta Llama 3 70B requires at least 20.4 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run Hermes 2 Theta Llama 3 70B locally?

Yes — Hermes 2 Theta Llama 3 70B can run locally on consumer hardware. At Q4_K_M quantization it needs 43.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Hermes 2 Theta Llama 3 70B?

At Q4_K_M, Hermes 2 Theta Llama 3 70B can reach ~67 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: AMD Instinct MI300X5300 ÷ 43.3 × 0.55 = ~67 tok/s

Estimated speed at Q4_K_M (43.3 GB)

~67 tok/s
~50 tok/s
~42 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Hermes 2 Theta Llama 3 70B?

At Q4_K_M, the download is about 42.33 GB. The full-precision Q8_0 version is 70.55 GB. The smallest option (IQ2_XXS) is 19.40 GB.

Which GPUs can run Hermes 2 Theta Llama 3 70B?

No single consumer GPU has enough VRAM to run Hermes 2 Theta Llama 3 70B at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.

Which devices can run Hermes 2 Theta Llama 3 70B?

11 devices with unified memory can run Hermes 2 Theta Llama 3 70B at Q4_K_M (43.3 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.